Mid Sweden University

PlayerLoad™ is the most widely used accelerometer-derived metric for quantifying external demands in sport. Its normalized variant, PlayerLoad·min−1, is also commonly used as a marker of exercise intensity. However, recent literature has raised concerns regarding its scientific foundation, including inconsistent definitions, arbitrary units, opaque filtering methods, questionable theoretical underpinnings, and imprecise mechanical terminology. The construct validity of PlayerLoad™ remains unverified, and emerging evidence suggests weak dose–response relationships with performance outcomes. Although widely adopted in practice, these concerns warrant critical scientific scrutiny. This review critically evaluates the validity and reliability of the PlayerLoad™ metric, highlighting the need for greater transparency and theoretical rigor in wearable athlete monitoring. Furthermore, we present alternative accelerometer-derived metrics, developed from clearer biomechanical and physiological principles, which may offer more robust and interpretable measures for researchers and practitioners. 

Purpose: This study introduces two-dimensional (2D) Kernel Density Estimation (KDE) plots as a novel tool for visualising Training Intensity Distribution (TID) in biathlon. The goal was to assess how KDE plots, alongside traditional training metrics, might provide a more detailed understanding of heart rate (HR) intensity patterns, aiding in the evaluation of training quality and compliance.

Methods: Fifteen elite-level youth biathletes from two national academy programmes were monitored over 5–6 weeks using HR monitors. Training sessions were measured via time-in-zone (TIZ) within a five-zone HR model with any time accumulated below the threshold for Zone 1, considered Zone 0. Sessions were dichotomised into those planned as low-intensity training (LIT) or those planned with high-intensity training (HIT). KDE analyses were conducted in MATLAB (Version R2020b) using the “ksdensity” function to create 2D KDE plots that visualise HR intensity accumulation across each programme, session type (e.g., Low-intensity training: LIT; High-intensity training: HIT), and individual athlete responses. Traditional histogram plots and grouped bar charts were also used for comparison.

Results: For LIT sessions, athletes performed less time in Zone 1 than planned, while performed time exceeded planned time in Zone 2. For HIT sessions, performed time in Zone 5 was lower than planned. All sessions contained unplanned time in Zone 0. The 2D KDE plots provided a continuous and detailed representation of HR intensity accumulation throughout training sessions, revealing patterns and intensity fluctuations that complement traditional TIZ analyses.

Conclusions: 2D KDE plots might serve as a valuable complementary tool for assessing TID in biathlon, offering a more nuanced and continuous view of HR intensity. By identifying discrepancies between planned and performed training intensity, coaches can refine strategies and provide individualised feedback. Incorporating KDE plots into training monitoring could improve training alignment, helping reduce overtraining or undertraining risks and optimising athlete development.

Sensor technology is often used across many sports by coaches, practitioners, and researchers. The wide range of sensors and numerous possibilities can be both interesting and daunting. Here, we explore the use of sensor technologies, focusing on Global Navigation Satellite Systems, Inertial Measurement Units, and heart rate monitoring for analyses of Para-Nordic skiing. We discuss current applications and future possibilities as well as some challenges and limitations. For example, we discuss what we may learn from analyses of skiing speed or sub-technique selection throughout a racecourse. We also offer ideas for how coaches, practitioners, and researchers can use these sensors in their work to further both the understanding and performance of Para-Nordic skiing. 

This study aimed to optimise performance prediction in short-course swimming through Principal Component Analyses (PCA) and multiple regression. All women’s freestyle races at the European Short-Course Swimming Championships were analysed. Established performance metrics were obtained including start, free-swimming, and turn performance metrics. PCA were conducted to reduce redundant variables, and a multiple linear regression was performed where the criterion was swimming time. A practical tool, the Potential Predictor, was developed from regression equations to facilitate performance prediction. Bland and Altman analyses with 95% limits of agreement (95% LOA) were used to assess agreement between predicted and actual swimming performance. There was a very strong agreement between predicted and actual swimming performance. The mean bias for all race distances was less than 0.1s with wider LOAs for the 800 m (95% LOA −7.6 to + 7.7s) but tighter LOAs for the other races (95% LOAs −0.6 to + 0.6s). Free-Swimming Speed (FSS) and turn performance were identified as Key Performance Indicators (KPIs) in the longer distance races (200 m, 400 m, 800 m). Start performance emerged as a KPI in sprint races (50 m and 100 m). The successful implementation of PCA and multiple regression provides coaches with a valuable tool to uncover individual potential and empowers data-driven decision-making in athlete training. 

This study compared performance strategies and sub-technique selection in cross-country skate skiing sprint races, specifically individual time-trial (ITT) and head-to-head (H2H) formats. Fourteen male cross-country skiers from the Chinese national team participated in the FIS-sanctioned sprint race day. GNSS and heart rate sensors recorded positioning, skiing speeds, heart rate, sub-technique usage, and skiing kinematics. Statistical parametric mapping (SPM) was used to determine the course positions (clusters) where instantaneous skiing speed was significantly associated with section time. One-way analyses of variance were used to examine differences between the ITT and H2H. H2H race speeds were 2.4 +/- 0.2% faster than the ITT race (p < 0.05).Variations in sub-technique and skiing kinematics were observed between race formats, indicating different strategies and tactics employed by athletes. SPM identified specific clusters (primarily uphill) where the fastest athlete gained significant time over the slowest. The greatest time gains were associated with higher G3 sub-technique usage and longer G3 cycle length on steep uphill terrain (9-13% gradients). Integrating SPM analyses and sub-technique assessments can help optimise performance and tactics in sprint races. This study enhances our understanding of cross-country skiing dynamics and performance variations among elite competitors.

Purpose To examine the micro-pacing (within-lap) strategies during biathlon skiing with and without the biathlon rifle. Methods Twenty biathletes (7 women, 13 men) performed two crosscountry skiing time-trials on a ≈2300 m course, once with and once without the biathlon rifle. During time-trials, biathletes wore a sensor that recorded position, distance and skiing-speed. A trajectory correction and statistical parametric mapping procedure determined the course positions (clusters) where instantaneous skiing-speed was significantly related to time-trial performance. The time differences between the fastest and slowest skier in these clusters were calculated. Results The fastest biathletes skied with greater instantaneous speeds in specific clusters, which included both uphill and downhill sections. The clusters represented time gains for the fastest skier over the slowest skier of between 16.1 and 25.8 s for the women and between 18.9 and 21.9 s for the men. The largest time gains between the fastest and slowest biathletes were observed in a downhill section that was preceded by a 180° turn, where time gains were between 2.9 and 4.1 s in clusters of between just 12 to 62 m. In biathlon skiing with-rifle, there were more clusters that were not present during without-rifle. When skiing with-rifle, there were additional clusters in the uphill sections that represented time gains of 5.2 s and 2.3 s for the women and men, respectively. Conclusions Statistical parametric mapping can be used in biathlon to provide pacing and performance feedback to athletes and coaches.

ObjectiveThis study aimed to identify Key Performance Indicators (KPIs) for men’s swimming strokes using Principal Component Analysis (PCA) and Multiple Regression Analysis to enhance training strategies and performance optimization. The analyses included all men’s individual 100 m races of the 2019 European Short-Course Swimming Championships.

ResultsDuration from 5 m prior to wall contact (In5) emerged as a consistent KPI for all strokes. Free Swimming Speed (FSS) was identified as a KPI for 'continuous' strokes (Breaststroke and Butterfly), while duration from wall contact to 10 m after (Out10) was a crucial KPI for strokes with touch turns (Breaststroke and Butterfly). The regression model accurately predicted swim times, demonstrating strong agreement with actual performance. Bland and Altman analyses revealed negligible mean biases: Backstroke (0% bias, LOAs − 2.3% to + 2.3%), Breaststroke (0% bias, LOAs − 0.9% to + 0.9%), Butterfly (0% bias, LOAs − 1.2% to + 1.2%), and Freestyle (0% bias, LOAs − 3.1% to + 3.1%). This study emphasizes the importance of swift turning and maintaining consistent speed, offering valuable insights for coaches and athletes to optimize training and set performance goals. The regression model and predictor tool provide a data-driven approach to enhance swim training and competition across different strokes.

Background: The Covid-19 pandemic in 2020 led to disruption of sporting events, with athletes obliged to comply with national lockdown restrictions.

Purpose: To investigate the effect of the Covid-19 pandemic restrictions on national-team XC skiers' annual and weekly training distribution from training diaries, results from submaximal and maximal physiological roller ski tests, and competition results from the International Ski and Snowboard Federation (FIS) world cup.

Methods: Annual and weekly training type (specific, non-specific, strength, other) and intensity distribution (TID) data were collected for 12 German XC-skiers (Tier 4/5; BM: 67 ± 7 kg; age 26 ± 3 years; 6♀: V̇O2max 61.3 ± 3.4 ml · kg · min−1; 6♂: V̇O2max 72.5 ± 6.2 ml · kg · min−1). TID was categorized using a 5-zone scale with Zones 1–2 representative of intensities below the first lactate threshold (LT1), zone 3 between LT1 and LT2, and zones 4–5 above LT2. Training data were grouped by lockdown periods in season 20/21 (L1/L2) and compared to data from the corresponding weeks in 19/20 (C1/C2). Laboratory testing was performed in the general preparation period prior to competition for both seasons. Differences between seasons (C1/C2 vs. L1/L2) in training and performance variables were analysed using repeated-measures ANOVA and linear mixed models.

Results: Total annual training duration increased by 9% during 20/21 (928 ± 79 h · year−1) compared to 19/20 (852 ± 73 h · year−1). During L1, skiers achieved a greater weekly training duration (mean differences (Δx¯: 7.7 h · week−1) compared to C1, due to an increase in non-specific training (Δx¯: 7.0 h · week−1), whereas L2 resulted in greater weekly training compared with C2 due to a higher specific endurance training volume (Δx¯: 1.4 h · week−1). In 20/21 skiers performed a higher volume of Zone 1 (Δx¯: 149 h · year−1). Laboratory test- and FIS racing performance improved from 19/20 to 20/21.

Conclusion: German XC skiers' training characteristics, laboratory- and racing performance were significantly different between the two seasons. In fact, training duration as well as laboratory- and racing performance increased from 19/20 to 20/21. In spite of seasonal variation in performance and training within an Olympic cycle these findings might suggest that skiers adapted their training effectively to pandemic constraints, ultimately enhancing performance outcomes.

Background:

Compared with other major global team sports such as football or basketball, ice hockey has received considerably less attention in sport-science research. However, the research focus on ice hockey performance is growing rapidly. Unfortunately, despite the growing interest in ice hockey, among the little research that has been conducted there are inconsistencies in terminology and methodology in the study of physiology and performance during games. The need for systematic and standardized reporting of study methodology is vital, as a lack of methodological detail or methodological inconsistencies make it impossible to replicate published studies, and alterations in the methodologies used can influence the measured demands imposed on players. Accordingly, this prohibits the ability of coaches to generate game-replicating training programs, decreasing the application of research findings to practice. In addition, a lack of methodological detail or methodological inconsistencies can result in incorrect conclusions being made from research.

Purpose:

In this invited commentary, we aim to increase awareness regarding the current standard of methodological reporting in ice hockey game-analysis research. In addition, we have developed a framework for the standardization of game analysis in ice hockey in order to allow for greater replication in future research and to increase the application of published findings to practice.

Conclusions:

We implore researchers in the field to consult the Ice Hockey Game Analysis Research Methodological Reporting Checklist in order to adopt a detailed reporting standard of methodologies in future work to help improve the applicability of research outcomes.

Aim To identify how riding rein direction (left and right) and rider asymmetry affect tölt performance in Icelandic horses. Methods Two horses were ridden in tölt by four riders on both left and right reins. Riders wore pressure insoles that measured the total absolute force (FAbs) and absolute force difference (FDiff) in their left and right feet in the stirrups. A 3D motion-analysis system recorded the degrees of side-to-side movement in the pelvis (RollP) and in the thoracolumbar region (RollT). Lateral advanced placement (LAP) and duty factor (DF) were calculated to determine tölt performance. One-way ANOVAs were used to assess the effect of rein direction on rider asymmetry variables (FAbs, FDiff, RollP and RollT) and tölt performance (LAP, DF) on a group level (n = 8). Within-subject Spearman rank correlations (ρ) were computed to determine the effect of rider asymmetry variables on tölt performance on an individual level. Results LAP was closer to 25% on the left rein compared to the right rein (mean difference: 1.8 ±1.2%; F(1,7) = 16.333; p = 0.005, η2p = 0.700). In addition, DF was lower on the left rein compared to the right rein (mean difference: 1.9±0.8%; F(1,7) = 41.299; p<0.001, η2p = 0.855). Individual relationships between RollT and LAP ranged from small negative to very large positive and reached significance for one rider (ρ = 0.730; p = 0.040). Individual relationships between RollP and DF ranged from very large negative to very large positive and reached significance for two riders (ρ = 0.731; p = 0.040; ρ = -0.723 p = 0.043). Conclusion Rein direction might influence tölt performance. Individual relationships between rider asymmetry and tölt performance were highly variable and reached significance in some instances, indicating that the relationship between rider asymmetry and tölt performance is highly individual. This type of biomechanical data can be used to provide valuable feedback to guide equestrians and coaches.